{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:01:03Z","timestamp":1770465663836,"version":"3.49.0"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Germany&#x0027;s Excellence Strategy","award":["EXC-2070"],"award-info":[{"award-number":["EXC-2070"]}]},{"name":"Germany&#x0027;s Excellence Strategy","award":["390732324"],"award-info":[{"award-number":["390732324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1109\/lra.2023.3320018","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T17:43:29Z","timestamp":1695836609000},"page":"7448-7455","source":"Crossref","is-referenced-by-count":7,"title":["Unsupervised Pre-Training for 3D Leaf Instance Segmentation"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7101-0497","authenticated-orcid":false,"given":"Gianmarco","family":"Roggiolani","sequence":"first","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2815-5760","authenticated-orcid":false,"given":"Federico","family":"Magistri","sequence":"additional","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7853-5510","authenticated-orcid":false,"given":"Tiziano","family":"Guadagnino","sequence":"additional","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6483-0319","authenticated-orcid":false,"given":"Jens","family":"Behley","sequence":"additional","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1173-6972","authenticated-orcid":false,"given":"Cyrill","family":"Stachniss","sequence":"additional","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Pre-clustering point clouds of crop fields using scalable methods","author":"nelson","year":"2022"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412483"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00171"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00964"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1093\/aob\/mcab078"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02060"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"ref30","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","author":"zbontar","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01281"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2017.11.020"},{"key":"ref32","article-title":"Representation learning with contrastive predictive coding","author":"oord","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2794619"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"6148","DOI":"10.1073\/pnas.1707462114","article-title":"Opinion: Smart farming is key to developing sustainable agriculture","volume":"114","author":"walter","year":"0","journal-title":"Proc Nat Acad Sci"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"ref39","first-page":"2294","article-title":"Quickshift: Provably good initializations for sample-based mean shift","author":"jiang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.gvc.2022.200057"},{"key":"ref18","article-title":"Deep leaf segmentation using synthetic data","author":"ward","year":"0","journal-title":"Proc Brit Mach Vis Conf (BMVC)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cj.2021.10.010"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3142440"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2866056"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref26","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"ester","year":"0","journal-title":"Proc Knowl Discovery Data Mining"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"ref25","article-title":"PointCNN: Convolution on X-transformed points","author":"li","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref47","first-page":"160","article-title":"Density-based clustering based on hierarchical density estimates","author":"campello","year":"0","journal-title":"Proc Knowl Discovery Data Mining"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-019-0490-0"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.227"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/367766.368168"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106310"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3288383"},{"key":"ref21","article-title":"Mask3D for 3D semantic instance segmentation","author":"schult","year":"0","journal-title":"Proc IEEE Int Conf Robot Automat"},{"key":"ref43","article-title":"Decoupled weight decay regularization","author":"loshchilov","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"ref27","article-title":"MIX-NET: Deep learning-based point cloud processing method for segmentation and occlusion leaf restoration of seedlings","volume":"11","author":"han","year":"2022","journal-title":"Plant"},{"key":"ref29","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2015.00167"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00302"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1046\/j.1466-822X.2003.00026.x"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA51227.2020.9363407"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/drones5020034"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160918"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21937"},{"key":"ref40","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017","journal-title":"arXiv 1708 04552"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/10254630\/10265122.pdf?arnumber=10265122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T18:26:37Z","timestamp":1698085597000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10265122\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":48,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/lra.2023.3320018","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11]]}}}